Publish v0.2.0 Announcement Blog (#43)

Signed-off-by: Steve Scargall <37674041+sscargal@users.noreply.github.com>

Author: sscargal

content/en/blog/2025/12/0.2.0-announcement/featured_image.png

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+---
+title: "Announcing MemMachine v0.2.0: The Next Evolution of AI Memory"
+date: 2025-12-09T21:30:00Z
+featured_image: "featured_image.png"
+tags: ["Release", "MemMachine", "AI Agent", "SDK", "MCP", "Semantic Memory"]
+author: "Steve Scargall"
+description: "Unlock the full potential of your AI agents with MemMachine v0.2.0. Discover our complete rearchitecture, powerful new SDKs, enhanced MCP integration, and the game-changing shift to Episodic and Semantic AI Agent Memory."
+aliases:
+---
+
+We are thrilled to announce the release of **MemMachine v0.2.0**, a major milestone that brings a complete redesign and rearchitecture of our memory system. This release introduces powerful new capabilities for AI Agent developers, including a shift to **Episodic and Semantic Memory**, native **MCP support**, and robust **Python SDKs**.
+
+## Highlights
+
+-   **Episodic and Semantic Memory**: "Profile" memory is now "Semantic" memory, reflecting its broader capabilities.
+-   **New Architecture**: A reimagined ingestion and search pipeline for better performance and accuracy.
+-   **Python SDKs**: Official Client and Server SDKs for seamless integration.
+-   **MCP Support**: Native implementation of the Model Context Protocol.
+-   **API v2**: A cleaner, more powerful REST API.
+
+---
+
+## From "Profile" to "Episodic and Semantic" Memory
+
+In v0.2.0, we have renamed "Profile" memory to **Semantic Memory**. While "Profile" implied a focus on user attributes, our system has evolved to capture a much wider range of semantic information—facts, world knowledge, and complex relationships derived from interactions. This rename aligns with our vision of providing a comprehensive long-term memory store that goes beyond simple user profiling.
+
+## A Reimagined Architecture
+
+We've completely rewritten our core architecture to address the limitations of the previous DeclarativeMemory system. The new design focuses on simplicity, performance, and scalability.
+
+### 1. Ingestion Pipeline
+Our new ingestion process is designed to maximize context and retrieval quality:
+-   **Derivative Extraction**: We extract raw sentences from message-type episodes using NLTK.
+-   **Context Augmentation**: Sentences are augmented with timestamps and source information.
+-   **Derivative Embedding**: These augmented sentences are embedded into vectors and stored in a vector database, pointing back to their originating episodes.
+-   **2-Tier Persistence**: We now persist data in two tiers: **Episodes** (raw content) and **Derivatives** (embedded chunks linked to episodes).
+
+### 2. Advanced Search Workflow
+Search is now more intelligent and context-aware:
+-   **Vector Similarity**: Queries are embedded as-is to find matches in the derivative vector database.
+-   **Context Expansion**: Matched derivatives trigger a context expansion, pulling in 1 episode backward and 2 episodes forward to reconstruct the full narrative.
+-   **Reranking**: Expanded contexts are reranked to ensure the most relevant information surfaces first.
+-   **Smart Limits**: If the search limit is reached, we prioritize episodes closest to the vector-matched nucleus.
+
+### Why This Matters
+This new architecture solves several critical pain points:
+-   **Performance**: Optimized database queries and efficient vector search.
+-   **Simplicity**: Configuration is now straightforward, removing the complexity of the old DeclarativeMemory.
+-   **Robustness**: The system is no longer sensitive to insertion order, making batch processing easier.
+-   **First-Class Properties**: Timestamps and sources are now first-class properties, simplifying filtering and indexing.
+
+---
+
+## New Python SDKs
+
+We are introducing two new Python SDKs to make building with MemMachine easier than ever.
+
+### Client Python SDK
+The new **Client SDK** (`memmachine.rest_client`) allows you to integrate MemMachine into your applications with just a few lines of code. It handles authentication, project management, and memory operations seamlessly.
+
+```python
+from memmachine import MemMachineClient
+
+client = MemMachineClient(base_url="http://localhost:8080")
+project = client.create_project(org_id="my_org", project_id="my_agent", description="Memory store for customer support agent")
+memory = project.memory(user_id="user123", agent_id="support_bot_01",session_id="session_555")
+
+# Add a memory
+memory.add(content="I am strictly vegetarian and I love spicy food.", role="user", metadata={"topic": "food_preference"})
+
+# Search memory
+results = memory.search("What should I suggest for dinner?")
+print(results)
+```
+
+For more information, see the [Client SDK documentation](https://docs.memmachine.ai/api_reference/python/client).
+
+### Python Server SDK
+For developers who want to embed MemMachine directly or build custom server implementations, the **Server SDK** (`memmachine-server`) provides direct access to the core memory logic and storage engines.
+
+---
+
+## Model Context Protocol (MCP) Support
+
+MemMachine v0.2.0 includes native support for the **Model Context Protocol (MCP)**. This means MemMachine can now be instantly used as a memory tool by any MCP-compliant agent or IDE.
+
+We expose two core tools via MCP:
+-   `add_memory`: Store important information, facts, and preferences.
+-   `search_memory`: Retrieve relevant context and long-term knowledge.
+
+This allows agents to automatically manage their own memory without custom integration code.
+
+---
+
+## Integrations
+
+We are committed to making MemMachine available wherever you build your agents. We are excited to announce integrations with leading platforms:
+
+-   **Claude Code**: Seamlessly give your Claude agents long-term memory.
+-   **GPT Store**: Enhance your custom GPTs with persistent context.
+-   **LangGraph**: Easily plug MemMachine into your LangGraph workflows.
+
+And this is just the beginning—we have plans to add support for many more platforms soon!
+
+---
+
+## Get Started
+
+MemMachine v0.2 delivers significant advancements in conversational memory and efficiency, establishing itself as one of the highest-scoring AI memory systems available on the LoCoMo benchmark.
+
+**Ready to experience the benefits of MemMachine v0.2?**
+
+- 👉 [Download and try MemMachine on GitHub](https://github.com/MemMachine/MemMachine) yourself. Get started today and see the performance firsthand.
+- 📖 [Explore the comprehensive documentation](https://docs.memmachine.ai) to discover integration guides, workflows, and advanced features.
+- 💬 [Join our Discord community](https://discord.gg/usydANvKqD) to connect with fellow developers, share feedback, and collaborate with teams already building innovative solutions on top of MemMachine.
+
+Don’t miss the opportunity to join a fast-growing ecosystem of organizations and engineers leveraging MemMachine for state-of-the-art conversational AI. Your feedback and contributions are welcome!
+
+We can't wait to see what you build with this new foundation!